Thoughts on learning and work

Day 10 of 180 Days of Data Viz #jdfi


I’m doing some form of data visualization learning for 180 days straight because I need to #JFDI.  See post explaining how and why I’m doing this.

A note on reviewing and repetition today, I’ve really discovered the joy of writing and how well it helps me absorb information.  I know this from writing coding exercises and learning languages.  But five years of working in Silicon Valley and two years of grad school later, I’d become hopelessly dependent on typing everything, which is great for most activities.  However, writing forces me to slow down to absorb concepts rather than just copy.  Some chemistry definitely happens.  Here’s what the Scientific American has to say about that.  So much return of analog lately.

Visualization Worked On or Created: 

N/A Today  – > Focusing on the completing tutorial/exploration work (finishing Scott Murray’s book, D3.js in Udacity in Treehouse)  that will be more mentally taxing when I’m working full-time.

Decomposition of a Visualization:

Cyberwar, Visualized

Code Learning:

Udacity Problem Set One Finished – On section 2a Design Principles

Three Takeaways

Treehouse Adding Axes to Visualization

Three Takeaways

Three Takeaways

Aligned Left

Reading and Learning Data Visualization Theoretically/Critically:

When Should I Use Logarithmic Scales in My Charts and Graphs?

Three Takeaways 

  • Use logarithmic scales to respond to skewness towards large values in cases which one or a few data points are much larger than the bulk of that data or to show percent change or multiplicative factors
  • Dot plots > bar charts for log data since bar charts, because they are judged by the length of the bar, will have a distorted meaning.
  • Can show values of change over time better than absolute numbers.

Bullet Graph Design Specification

Three Takeaways

  • Specifically, bullet graphs support the comparison of the featured measure to one or more related measures (for example, a target or the same measure at some point in the past, such as a year ago) and relate the featured measure to defined quantitative ranges that declare its qualitative state (for example, good, satisfactory, and poor). Its linear design not only gives it a small footprint, but also supports more efficient reading than radial meters.
    • Text label
      • • A quantitative scale along a single linear axis
      • • The featured measure
      • • One or two comparative measures (optional)
      • • From two to five ranges along the quantitative scale to declare the featured measure’s qualitative state (optional)
  • Add color hues and saturation to the richness of using this tool.